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Improving Clinical Decision-Making and Treatment Research for Shoulder Tendinopathy through Multimodal Deep Learning Analysis

Improving Clinical Decision-Making and Treatment for Rotator Cuff Pathology through a Multimodal Deep Learning Model Integrating Clinical Symptoms and Imaging Data

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500104956
Enrollment
Unknown
Registered
2025-06-26
Start date
2025-06-30
Completion date
Unknown
Last updated
2025-08-25

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Rotator Cuff Tendinopathy

Interventions

Independent diagnosis of the junior physician group, model for the junior physician group and senior physician group.:None

Sponsors

Department of Sports Medicine, Sun Yat-sen Memorial Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1. Age 18 years or older; 2. Unilateral shoulder cuff injur diagnosed by MRI and/or arthroscopy; 3. Availability of EMR, X-ray, and MRI dataset;

Exclusion criteria

Exclusion criteria: 1. A prior history of surgical intervention on the shoulder; 2. Concurrent shoulder pathology including tumors, infection etc; 3. Missing over 30% data in EMR.

Design outcomes

Primary

MeasureTime frame
Area Under the Receiver Operating Characteristic Curve;—

Secondary

MeasureTime frame
Area Under the Precision-Recall Curve;sensitivity;Specificity;—

Countries

China

Contacts

Public ContactYang Rui

Sun Yat-sen Memorial Hospital

yangr@mail.sysu.edu.cn+86 136 9420 0667

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026